Statistical model-based metrology

ABSTRACT

Methods and systems for creating a measurement model based on measured training data are presented. The trained measurement model is used to calculate process parameter values, structure parameter values, or both, directly from measured data collected from other wafers. The measurement models receive measurement data directly as input and provide process parameter values, structure parameter values, or both, as output. The measurement model enables the direct measurement of process parameters. Measurement data from multiple targets is collected for model building, training, and measurement. In some examples, the use of measurement data associated with multiple targets eliminates, or significantly reduces, the effect of under layers in the measurement result, and enables more accurate measurements. Measurement data collected for model building, training, and measurement, may be derived from measurements performed by a combination of multiple, different measurement techniques.

CROSS REFERENCE TO RELATED APPLICATION

The present application for patent claims priority under 35 U.S.C. § 119from U.S. provisional patent application Ser. No. 61/805,831, entitled“Optical Metrology Using Statistical Models for Direct Measurement ofStructure and Process Parameters,” filed Mar. 27, 2013, the subjectmatter of which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The described embodiments relate to metrology systems and methods, andmore particularly to methods and systems for improved parametermeasurement.

BACKGROUND INFORMATION

Semiconductor devices such as logic and memory devices are typicallyfabricated by a sequence of processing steps applied to a specimen. Thevarious features and multiple structural levels of the semiconductordevices are formed by these processing steps. For example, lithographyamong others is one semiconductor fabrication process that involvesgenerating a pattern on a semiconductor wafer. Additional examples ofsemiconductor fabrication processes include, but are not limited to,chemical-mechanical polishing, etch, deposition, and ion implantation.Multiple semiconductor devices may be fabricated on a singlesemiconductor wafer and then separated into individual semiconductordevices.

Metrology processes are used at various steps during a semiconductormanufacturing process to detect defects on wafers to promote higheryield. Optical metrology techniques offer the potential for highthroughput without the risk of sample destruction. A number of opticalmetrology based techniques including scatterometry and reflectometryimplementations and associated analysis algorithms are commonly used tocharacterize critical dimensions, film thicknesses, composition andother parameters of nanoscale structures.

Traditionally, optical metrology is performed on targets consisting ofthin films and/or repeated periodic structures. During devicefabrication, these films and periodic structures typically represent theactual device geometry and material structure or an intermediate design.As devices (e.g., logic and memory devices) move toward smallernanometer-scale dimensions, characterization becomes more difficult.Devices incorporating complex three-dimensional geometry and materialswith diverse physical properties contribute to characterizationdifficulty.

For example, modern memory structures are often high-aspect ratio,three-dimensional structures that make it difficult for opticalradiation to penetrate to the bottom layers. In addition, the increasingnumber of parameters required to characterize complex structures (e.g.,FinFETs), leads to increasing parameter correlation. As a result, themeasurement model parameters characterizing the target often cannot bereliably decoupled.

In response to these challenges, more complex optical tools have beendeveloped. Measurements are performed over a large ranges of severalmachine parameters (e.g., wavelength, azimuth and angle of incidence,etc.), and often simultaneously. As a result, the measurement time,computation time, and the overall time to generate reliable results,including measurement recipes, increases significantly. In addition, thespreading of light intensity over large wavelength ranges decreasesillumination intensity at any particular wavelength and increases signaluncertainty of measurements performed at that wavelength.

In addition, existing model based metrology methods typically include aseries of steps to model and then measure structure parameters.Typically measurement data is collected (e.g., DOE spectra) from aparticular metrology target. An accurate model of the optical system,dispersion parameters, and geometric features is formulated. Filmspectra measurements are collected to determine material dispersions. Aparametric geometric model of the target structure is created along withan optical model. In addition, simulation approximations (e.g.,slabbing, Rigorous Coupled Wave Analysis (RCWA), etc.) must be carefullyperformed to avoid introducing excessively large errors. Discretizationand RCWA parameters are defined. A series of simulations, analysis, andregressions are performed to refine the geometric model and determinewhich model parameters to float. A library of synthetic spectra isgenerated. Finally, measurements are performed using the library and thegeometric model. Each step introduces errors and consumes a significantamount of computational and user time. Typically, a model building taskrequires days, or even weeks, to complete. In addition, the size of thelibrary and the computation time associated with performing regressioncalculations during measurement reduces the throughput of themeasurement system.

Future metrology applications present challenges for metrology due toincreasingly small resolution requirements, multi-parameter correlation,increasingly complex geometric structures, and increasing use of opaquematerials. Thus, methods and systems for improved measurements aredesired.

SUMMARY

Methods and systems for creating a measurement model based on measuredtraining data are presented. The trained measurement model is then usedto calculate process parameter values, structure parameter values, orboth, directly from measured data collected from other wafers.

In one aspect, the measurement models described herein receivemeasurement data directly as input and provide process parameter values,structure parameter values, or both, as output. By streamlining themodeling process, predictive results are improved along with a reductionin computation and user time.

The measurement model enables the direct measurement of processparameters, thus obviating the need for a separate model to deriveprocess parameters from geometric parameters. Because process variationis captured by the model, process parameter values are measured evenwhen the underlying structure topology is changing due to processvariations.

In a further aspect, measurement data from multiple targets is collectedfor model building, training, and measurement. In some examples, the useof measurement data associated with multiple targets eliminates, orsignificantly reduces, the effect of under layers in the measurementresult. The use of measurement data associated with multiple targetsincreases the sample and process information embedded in the model. Inparticular, the use of training data that includes measurements ofmultiple, different targets at one or more measurement sites enablesmore accurate measurements.

In another further aspect, measurement data derived from measurementsperformed by a combination of multiple, different measurement techniquesis collected for model building, training, and measurement. The use ofmeasurement data associated with multiple, different measurementtechniques increases the sample and process information embedded in themodel and enables more accurate measurements. In general, anymeasurement technique, or combination of two or more measurementtechniques may be contemplated.

In yet another aspect, the measurement model results described hereincan be used to provide active feedback to a process tool (e.g.,lithography tool, etch tool, deposition tool, etc.).

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not limiting in any way. Other aspects,inventive features, and advantages of the devices and/or processesdescribed herein will become apparent in the non-limiting detaileddescription set forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrative of a method 100 of building andtraining a measurement model as described herein.

FIG. 2 is a flowchart illustrative of a method 110 of building andtraining a measurement model in another example as described herein.

FIG. 3 is a flowchart illustrative of a method 120 of building andtraining a measurement model in yet another example as described herein.

FIG. 4 is a flowchart illustrative of a method 130 of building andtraining a measurement model in yet another example as described herein.

FIG. 5 is a flowchart illustrative of a method 140 of measuring processparameters, structural parameters, or both, of a semiconductor waferusing a measurement model generated by any of methods 100, 110, 120, and130.

FIGS. 6A-6B are contour plots illustrative of measurements of exposuredosage and depth of focus, respectively, of a FEM wafer.

FIGS. 6C-6D are contour plots illustrative of measurements of exposuredosage and depth of focus, respectively, of a sample wafer.

FIGS. 6E-6F are contour plots illustrative of measurements of exposuredosage and depth of focus, respectively, of another sample wafer.

FIGS. 7A-7B are contour plots illustrative of measurements of middlecritical dimension (MCD) of isolated structures and dense structures,respectively, across the surface of a focus exposure matrix (FEM) wafer.

FIGS. 8A-8B are contour plots illustrative of measurements of middlecritical dimension (MCD) of isolated structures and dense structures,respectively, across the surface of a sample wafer.

FIGS. 9A-9B are contour plots illustrative of measurements of depth offocus and exposure dosage, respectively, across the surface of a FEMwafer.

FIGS. 10A-10B are contour plots illustrative of measurements of depth offocus and exposure dosage, respectively, across the surface of a samplewafer.

FIG. 11 is a diagram illustrative of a matrix 170 of principalcomponents maps of spectra collected from a FEM wafer.

FIG. 12 illustrates a system 300 for measuring characteristics of aspecimen in accordance with the exemplary methods presented herein.

DETAILED DESCRIPTION

Reference will now be made in detail to background examples and someembodiments of the invention, examples of which are illustrated in theaccompanying drawings.

Methods and systems for creating a measurement model based only onmeasured training data (e.g., spectra collected from a Design ofExperiments (DOE) wafer) are presented. The trained measurement model isthen used to calculate process parameter values, structure parametervalues, or both, directly from measured data (e.g., spectra) collectedfrom other wafers.

In one aspect, the measurement models described herein receivemeasurement data (e.g., measured spectra) directly as input and provideprocess parameter values, structure parameter values, or both, asoutput. By streamlining the modeling process, the predictive results areimproved along with a reduction in computation and user time.

In a further aspect, process parameters are directly measured based onthe measurement model created from raw measurement data (e.g., spectra)as described herein. Thus, a separate model to derive process parametersfrom geometric parameters is not required. Because process variation iscaptured by the model, process parameter values are measured even whenthe underlying structure topology is changing due to process variations.This is impossible, or very difficult to model using existing modelbased metrology methods.

Traditionally, model-based semiconductor metrology consists offormulating a metrology model that attempts to predict the measuredoptical signals based on a model of the interaction of the measurementtarget with the particular metrology system. The target-specific modelincludes a parameterization of the structure in terms of the physicalproperties of the measurement target of interest (e.g., filmthicknesses, critical dimensions, refractive indices, grating pitch,etc.). In addition, the model includes a parameterization of themeasurement tool itself (e.g., wavelengths, angles of incidence,polarization angles, etc.).

Machine parameters (P_(machine)) are parameters used to characterize themetrology tool itself. Exemplary machine parameters include angle ofincidence (AOI), analyzer angle (A₀), polarizer angle (P₀), illuminationwavelength, numerical aperture (NA), etc. Specimen parameters (Pspecimen) are parameters used to characterize the geometric and materialproperties of the specimen. For a thin film specimen, exemplary specimenparameters include refractive index, dielectric function tensor, nominallayer thickness of all layers, layer sequence, etc.

For measurement purposes, the machine parameters are treated as known,fixed parameters and the specimen parameters, or a subset of specimenparameters, are treated as unknown, floating parameters. The floatingparameters are resolved by a fitting process (e.g., regression, librarymatching, etc.) that produces the best fit between theoreticalpredictions and measured data. The unknown specimen parameters,P_(specimen) are varied and the model output values are calculated untila set of specimen parameter values are determined that results in aclose match between the model output values and the measured values.

In many cases, the specimen parameters are highly correlated. This canlead to instability of the metrology-based target model. In some cases,this is resolved by fixing certain specimen parameters. However, thisoften results in significant errors in the estimation of the remainingparameters. For example, underlying layers (e.g., oxide base layers of asemiconductor material stack on a semiconductor wafer) are not uniformlythick over the surface of a wafer. However, to reduce parametercorrelation, measurement models are constructed that treat these layersas having a fixed thickness over the surface of the wafer.Unfortunately, this may lead to significant errors in the estimation ofother parameters.

By using raw measurement data (e.g., spectra) only to create the modelof process parameters, geometric parameters, or both, as describedherein, the errors and approximations associated with traditional modelbased metrology methods are reduced. Measurement of complex threedimensional structures and in-die measurements are enabled without thecomplexity added by geometric models and simulations. In some examples,the model can be created in less than an hour. In addition, by employinga simplified model, measurement time is reduced compared to existingmodel based metrology methods. In some examples, measurement time isless than ten milliseconds per measurement site. In contrast,measurement times using traditional model based metrology methods can begreater than one second in some cases.

FIG. 1 illustrates a method 100 suitable for implementation by ametrology system such as metrology system 300 illustrated in FIG. 12 ofthe present invention. In one aspect, it is recognized that dataprocessing blocks of method 100 may be carried out via a pre-programmedalgorithm executed by one or more processors of computing system 330, orany other general purpose computing system. It is recognized herein thatthe particular structural aspects of metrology system 300 do notrepresent limitations and should be interpreted as illustrative only.

In block 101, a first amount of measurement data associated withmeasurements of a first plurality of sites on a surface of asemiconductor wafer is received by a computing system (e.g., computingsystem 330). The measured sites exhibit known variations of at least oneprocess parameter, structure parameter, or both.

In some embodiments, process parameter variations are organized in aDesign of Experiments (DOE) pattern on the surface of a semiconductorwafer (e.g., DOE wafer). In this manner, the measurement sitesinterrogate different locations on the wafer surface that correspondwith different process parameter values. In one example, the DOE patternis a Focus/Exposure Matrix (FEM) pattern. Typically, a DOE waferexhibiting a FEM pattern includes a grid pattern of measurement sites.In one grid direction (e.g., the x-direction), the exposure dosage isvaried while the depth of focus is held constant. In the orthogonal griddirection (e.g., the y-direction), the depth of focus is varied whilethe exposure dosage is held constant. In this manner, measurement datacollected from the DOE wafer includes data associated with knownvariations in the focus and dosage process parameters.

In the aforementioned example, the measurement data is associated with aDOE wafer processed with known variations in focus and exposure.However, in general, measurement data associated with any knownvariation of process parameters, structural parameter, or both, may becontemplated.

In block 102, one or more features are extracted from the first amountof measurement data. In some examples, the measurement data is analyzedusing Principal Components Analysis (PCA), or non-linear PCA, to extractfeatures that most strongly reflect the variations in process parameter,structural parameters, or both, that are present at the differentmeasurement sites. In some other examples, a signal filtering techniquemay be applied to extract signal data that most strongly reflects theparameter variations present at the different measurement sites. In someother examples, individual signals that most strongly reflect theparameter variations present at the different measurement sites may beselected from multiple signals present in the measurement data.Although, it is preferred to extract features from the measurement datato reduce the dimension of data subject to subsequent analysis, it isnot strictly necessary. In this sense, block 102 is optional.

In block 103, an input-output measurement model is determined based onfeatures extracted from the measurement data, or alternatively, directlyfrom the measurement data. The input-output measurement model isstructured to receive measurement data generated by a metrology systemat one or more measurement sites, and directly determine processparameter values, structural parameter values, or both, associated witheach measurement target. In a preferred embodiment, the input-outputmeasurement model is implemented as a neural network model. In oneexample, the number of nodes of the neural network is selected based onthe features extracted from the measurement data. In other examples, theinput-output measurement model may be implemented as a polynomial model,a response surface model, or other types of models.

In block 104, an expected response model is generated for each of theparameters that are known to be varying across the measurement siteswhere the measurement data is collected. In general, the expectedresponse model defines the values of the known, varying parameters as afunction of location on the wafer surface. In this manner, the expectedresponse model defines the expected overall shape of the wafer map for agiven parameter.

In block 105, the input-output measurement model is trained based onparameter values determined from the expected response model. In thismanner, process information embedded in the expected response model isused to constrain the input-output model within the process space. Inthis manner, the trained input-output measurement model is generatedusing DOE measurement data and an expected response model. The model istrained such that its output fits the defined expected response for allthe spectra in the process variation space defined by the DOE spectra.

In some examples, one or more process parameters are to be measured. Inthese examples, the expected response model is based on the knownprocess parameter values associated with the measured DOE wafer.

FIG. 2 illustrates a method 110 suitable for implementation by ametrology system such as metrology system 300 illustrated in FIG. 12 ofthe present invention in another example. Method 110 includes likenumbered blocks as described with reference to FIG. 1. As depicted inFIG. 2, in block 111, known process parameter values are received by acomputing system such as computing system 330. In some examples, theknown process parameter values are the depth of focus and exposure doseof a lithography system used to manufacture the DOE wafer.

In block 112, computing system 330 determines an expected response modelfor each process parameter. In a typical DOE wafer, the focus andexposure are changed linearly in accordance with the x and y coordinatesof the DOE wafer. In some examples, the expected response shape for afocus parameter on a DOE wafer is a tilted plane in the x-direction witha zero crossing in the middle of the wafer. In one example, the expectedresponse function that determines the focus parameter value is,focus=a*x+b, where a and b are coefficients that realize the best fit tothe known focus parameter values at each measurement site. Similarly,the expected response shape for an exposure parameter on a DOE wafer isa tilted plane in the y-direction with a zero crossing in the middle ofthe wafer. In another example, the expected response function thatdetermines the exposure parameter value is, exposure=c*y+d, where c andd are coefficients that realize the best fit to the known exposureparameter values at each measurement site.

In another example, the expected response model of the DOE wafer isdetermined by fitting a two dimensional map function (i.e., {x,y}) tothe known values of focus and exposure at each of the measurement sites.

In some other examples, one or more structural parameters are to bemeasured. For a geometric parameter the shape of the wafer map may bemore complex, and often the shape is defined by the process. In some ofthese examples, the expected response model is generated based on theknown process parameter values associated with the measured DOE wafer.FIG. 3 illustrates a method 120 suitable for implementation by ametrology system such as metrology system 300 illustrated in FIG. 12 ofthe present invention in yet another example. Method 120 includes likenumbered blocks as described with reference to FIG. 1.

As depicted in FIG. 3, in block 121, known process parameter values arereceived by a computing system such as computing system 330. In oneexample, the known process parameter values are the known focus andexposure values corresponding with each measurement site.

In block 122, computing system 330 determines the expected structuralparameter values associated with each of the known process parametervalues at each measurement site are determined based on a simulation.For example, a process simulator is employed to define the expectedresponse of a structural parameter (i.e., a geometric or materialparameter) for a given set of process parameter values. An exemplaryprocess simulator includes the Positive Resist Optical Lithography(PROLITH) simulation software available from KLA-Tencor Corporation,Milpitas, Calif. (USA). Although this exemplary lithography processmodel is generated using PROLITH software, in general, any processmodeling technique or tool may be contemplated within the scope of thispatent document. In some examples, the expected structural parametervalues at each measurement site are determined based on thecorresponding focus and exposure parameter values corresponding witheach measurement site.

In block 123, computing system 330 determines the expected responsemodel of each structural parameter. In some examples, the expectedresponse model is determined by fitting a two dimensional (e.g., {x,y})map function to the structural parameter values associated with eachmeasurement site.

In some other examples, the expected response model for a structuralparameter is determined based on features of the measurement dataassociated with the DOE wafer. FIG. 4 illustrates a method 130 suitablefor implementation by a metrology system such as metrology system 300illustrated in FIG. 12 of the present invention in yet another example.Method 130 includes like numbered blocks as described with reference toFIG. 1.

As depicted in FIG. 4, in block 131, reference measurement dataassociated with measurements of the structural parameter on the DOEwafer are received, for example, by computing system 330. The referencemeasurement data is derived from measurements of targets at one or moremeasurement sites of the DOE wafer by a reference metrology system suchas a Scanning Electron Microscope (SEM), Tunneling electron Microscope(TEM), Atomic Force Microscope (AFM), or x-ray measurement system.

In addition, in block 102, one or more features (e.g., shape functions)are extracted from the measurement data as described with reference toFIG. 1. In one example, the first principal component (PC1) of themeasured spectra is used to describe the overall shape of the responsesurface associated with a particular structural parameter (e.g., MiddleCritical Dimension (MCD)).

In block 132, computing system 330 calibrates the shape function(s)extracted from the measurement data based on the reference measurementdata to generate a calibrated response surface.

In block 133, computing system 330 determines the expected responsemodel of each of the known structural parameters by fitting a twodimensional (e.g., {x,y}) map function to the calibrated responsesurface. In one example, the expected response model of the MCDparameter is: MCD=a₀₁+a₁₁(y+r₀y²)+a₂₁x², where x and y are the wafercoordinates and a01, a11, r0, a21 are coefficients that best fit thefunction to the calibrated shape function.

In another aspect, the trained model is employed as the measurementmodel for measurement of other wafers. FIG. 5 illustrates a method 140suitable for implementation by a metrology system such as metrologysystem 300 illustrated in FIG. 12 of the present invention. In oneaspect, it is recognized that data processing blocks of method 140 maybe carried out via a pre-programmed algorithm executed by one or moreprocessors of computing system 330, or any other general purposecomputing system. It is recognized herein that the particular structuralaspects of metrology system 300 do not represent limitations and shouldbe interpreted as illustrative only.

In block 141, an amount of measurement data associated with measurementsof a second plurality of sites on a surface of a semiconductor wafer isreceived by a computing system (e.g., computing system 330).

In block 142, one or more features are extracted from the measurementdata. In some examples, the measurement data is analyzed using PrincipalComponents Analysis (PCA), or non-linear PCA, to extract features thatmost strongly reflect the variations in process parameter, structuralparameters, or both, that are present at the different measurementsites. In some other examples, a signal filtering technique may beapplied to extract signal data that most strongly reflects the parametervariations present at the different measurement sites. In some otherexamples, individual signals that most strongly reflect the parametervariations present at the different measurement sites may be selectedfrom multiple signals present in the measurement data. Although, it ispreferred to extract features from the measurement data to reduce thedimension of data subject to subsequent analysis, it is not strictlynecessary. In this sense, block 142 is optional. In addition, it ispreferred to extract features from the measurement data using the sameanalysis employed to extract features from the training data in block102, as described with reference to FIGS. 1-4.

In block 143, at least one process parameter value, at least onestructural parameter value, or both, associated with each of the secondplurality of sites is determined by computing system 330 based on afitting of the second amount of measurement data to the trainedinput-output measurement model as described with reference to FIGS. 1-4,by way of non-limiting example.

In block 144, the determined parameter values are stored in a memory.For example, the parameter values may be stored on-board the measurementsystem 300, for example, in memory 332, or may be communicated (e.g.,via output signal 340) to an external memory device.

FIG. 6A is a contour plot 150 illustrative of measurements of exposuredosage across the surface of a DOE wafer. As illustrated, exposuredosage varies in the x-direction across the wafer and is constant in they-direction across the wafer. FIG. 6B is a contour plot 151 illustrativeof measurements of lithography depth of focus across the surface of aDOE wafer. As illustrated, depth of focus varies in the y-directionacross the wafer and is constant in the x-direction across the wafer.

The measurement results illustrated in FIGS. 6A and 6B result frommeasurements (i.e., measured spectra) collected at multiple measurementsites on a DOE wafer and processed in accordance with method 140illustrated in FIG. 5. The underlying dosage and focus measurementmodels were developed in accordance with method 110 and trained withmeasurement data collected from different measurement sites on the sameDOE wafer.

FIG. 6C is a contour plot 152 illustrative of measurements of exposuredosage across the surface of a sample wafer. FIG. 6D is a contour plot153 illustrative of measurements of lithography depth of focus acrossthe surface of the same wafer described with reference to FIG. 6C.

The measurement results illustrated in FIGS. 6C and 6D are each derivedfrom measurements (i.e., measured spectra) collected at multiplemeasurement sites on a wafer, different from the DOE wafer, andprocessed in accordance with method 140 illustrated in FIG. 5. Theunderlying dosage and focus measurement models were developed inaccordance with method 110 and trained with measurement data collectedfrom different measurement sites on the DOE wafer described withreference to FIGS. 6A and 6B.

FIG. 6E is a contour plot 154 illustrative of measurements of exposuredosage across the surface of yet another sample wafer. FIG. 6F is acontour plot 155 illustrative of measurements of lithography depth offocus across the surface of the same wafer described with reference toFIG. 6E.

The measurement results illustrated in FIGS. 6E and 6F are derived frommeasurements (i.e., measured spectra) collected at multiple measurementsites on the sample wafer that is different from the DOE wafer, andprocessed in accordance with method 140 illustrated in FIG. 5. Theunderlying dosage and focus measurement models were developed inaccordance with method 110 and trained with measurement data collectedfrom different measurement sites on the DOE wafer described withreference to FIGS. 6A and 6B. As illustrated in FIGS. 6C-6F, variationsin the value of focus and exposure across different wafers havingdifferent focus steps are captured by the measurement model developed inaccordance with method 110.

FIG. 7A is a contour plot 156 illustrative of measurements of middlecritical dimension (MCD) of isolated structures across the surface of afocus exposure matrix (FEM) wafer. The exposure dosage varies in thex-direction across the wafer and the depth of focus varies in they-direction across the wafer. As illustrated, the MCD values vary acrossthe surface of the wafer due to differences in focus and exposure dosageacross the surface of the wafer. FIG. 7B is a contour plot 157illustrative of measurements of middle critical dimension (MCD) of densestructures across the surface of a focus exposure matrix (FEM) wafer.The exposure dosage varies in the x-direction across the wafer and thedepth of focus varies in the y-direction across the wafer. Asillustrated, the MCD values for dense structures also vary across thesurface of the wafer due to differences in focus and exposure dosageacross the surface of the wafer, but in a different manner than isolatedstructures.

The measurement results illustrated in FIGS. 7A and 7B result frommeasurements (i.e., measured spectra) collected at multiple measurementsites on the FEM wafer and processed in accordance with method 140illustrated in FIG. 5. Both the underlying isolated MCD measurementmodel and the dense MCD measurement model were developed in accordancewith method 120 and trained with measurement data collected fromdifferent measurement sites on the same FEM wafer.

FIG. 8A is a contour plot 158 illustrative of measurements of MCD ofisolated structures across the surface of a sample wafer. FIG. 8B is acontour plot 159 illustrative of measurements of MCD of dense structuresacross the surface of a sample wafer. The sample wafers were processedat a particular depth of focus and exposure dosage. The particular focusand dosage values correspond approximately with the focus and dosagevalues near the middle of the FEM wafer measured in FIGS. 7A-7B.

The measurement results illustrated in FIGS. 8A and 8B are derived frommeasurements (i.e., measured spectra) collected at multiple measurementsites on a sample wafer, different from the FEM wafer, and processed inaccordance with method 140 illustrated in FIG. 5. Both the underlyingisolated MCD measurement model and the dense MCD measurement model weredeveloped in accordance with method 120 and trained with measurementdata collected from different measurement sites on FEM wafer.

The measurement results illustrated in FIGS. 6A-6F, 7A-7B, and 8A-8Bwere derived from different measurement models, each corresponding to aparticular process or structural parameter (e.g., focus, exposure, andMCD). However, in general, a measurement model may characterize morethan one process parameter, structural parameter, or both.

FIG. 12 illustrates a system 300 for measuring characteristics of aspecimen in accordance with the exemplary methods presented herein. Asshown in FIG. 12, the system 300 may be used to perform spectroscopicellipsometry measurements of one or more structures of a specimen 301.In this aspect, the system 300 may include a spectroscopic ellipsometerequipped with an illuminator 302 and a spectrometer 304. The illuminator302 of the system 300 is configured to generate and direct illuminationof a selected wavelength range (e.g., 150-850 nm) to the structure 310disposed on the surface of the specimen 301. In turn, the spectrometer304 is configured to receive illumination reflected from the surface ofthe specimen 301. It is further noted that the light emerging from theilluminator 302 is polarized using a polarization state generator 307 toproduce a polarized illumination beam 306. The radiation reflected bythe structure disposed on the specimen 301 is passed through apolarization state analyzer 309 and to the spectrometer 304. Theradiation received by the spectrometer 304 in the collection beam 308 isanalyzed with regard to polarization state, allowing for spectralanalysis by the spectrometer of radiation passed by the analyzer. Thesespectra 311 are passed to the computing system 330 for analysis of thestructure 310.

As depicted in FIG. 12, system 300 includes a single measurementtechnology (i.e., SE). However, in general, system 300 may include anynumber of different measurement technologies. By way of non-limitingexample, system 300 may be configured as a spectroscopic ellipsometer(including Mueller matrix ellipsometry), a spectroscopic reflectometer,a spectroscopic scatterometer, an overlay scatterometer, an angularresolved beam profile reflectometer, a polarization resolved beamprofile reflectometer, a beam profile reflectometer, a beam profileellipsometer, any single or multiple wavelength ellipsometer, or anycombination thereof. Furthermore, in general, measurement data collectedby different measurement technologies and analyzed in accordance withthe methods described herein may be collected from multiple tools,rather than one tool integrating multiple technologies.

In a further embodiment, system 300 may include one or more computingsystems 330 employed to perform measurements based on measurement modelsdeveloped in accordance with the methods described herein. The one ormore computing systems 330 may be communicatively coupled to thespectrometer 304. In one aspect, the one or more computing systems 330are configured to receive measurement data 311 associated withmeasurements of the structure 310 of specimen 301.

It should be recognized that the various steps described throughout thepresent disclosure may be carried out by a single computer system 330or, alternatively, a multiple computer system 330. Moreover, differentsubsystems of the system 300, such as the spectroscopic ellipsometer304, may include a computer system suitable for carrying out at least aportion of the steps described herein. Therefore, the aforementioneddescription should not be interpreted as a limitation on the presentinvention but merely an illustration. Further, the one or more computingsystems 330 may be configured to perform any other step(s) of any of themethod embodiments described herein.

In addition, the computer system 330 may be communicatively coupled tothe spectrometer 304 in any manner known in the art. For example, theone or more computing systems 330 may be coupled to computing systemsassociated with the spectrometer 304. In another example, thespectrometer 304 may be controlled directly by a single computer systemcoupled to computer system 330.

The computer system 330 of the metrology system 300 may be configured toreceive and/or acquire data or information from the subsystems of thesystem (e.g., spectrometer 304 and the like) by a transmission mediumthat may include wireline and/or wireless portions. In this manner, thetransmission medium may serve as a data link between the computer system330 and other subsystems of the system 300.

Computer system 330 of the integrated metrology system 300 may beconfigured to receive and/or acquire data or information (e.g.,measurement results, modeling inputs, modeling results, etc.) from othersystems by a transmission medium that may include wireline and/orwireless portions. In this manner, the transmission medium may serve asa data link between the computer system 330 and other systems (e.g.,memory on-board metrology system 300, external memory, a referencemeasurement source, or other external systems). For example, thecomputing system 330 may be configured to receive measurement data froma storage medium (i.e., memory 332 or an external memory) via a datalink. For instance, spectral results obtained using spectrometer 304 maybe stored in a permanent or semi-permanent memory device (e.g., memory332 or an external memory). In this regard, the spectral results may beimported from on-board memory or from an external memory system.Moreover, the computer system 330 may send data to other systems via atransmission medium. For instance, an integrated measurement model or aspecimen parameter 340 determined by computer system 330 may becommunicated and stored in an external memory. In this regard,measurement results may be exported to another system.

Computing system 330 may include, but is not limited to, a personalcomputer system, mainframe computer system, workstation, image computer,parallel processor, or any other device known in the art. In general,the term “computing system” may be broadly defined to encompass anydevice having one or more processors, which execute instructions from amemory medium.

Program instructions 334 implementing methods such as those describedherein may be transmitted over a transmission medium such as a wire,cable, or wireless transmission link. For example, as illustrated inFIG. 12, program instructions 334 stored in memory 332 are transmittedto processor 331 over bus 333. Program instructions 334 are stored in acomputer readable medium (e.g., memory 332). Exemplary computer-readablemedia include read-only memory, a random access memory, a magnetic oroptical disk, or a magnetic tape.

In a further aspect, measurement data from multiple targets is collectedfor model building, training, and measurement. In some examples, the useof measurement data associated with multiple targets eliminates, orsignificantly reduces, the effect of under layers in the measurementresult. In one example, measurement signals from two targets aresubtracted to eliminate, or significantly reduce, the effect of underlayers in each measurement result. The use of measurement dataassociated with multiple targets increases the sample and processinformation embedded in the model. In particular, the use of trainingdata that includes measurements of multiple, different targets at one ormore measurement sites enables more accurate measurements.

In one example, a measurement model is created from spectralmeasurements of a FEM wafer for both isolated and dense targets. Themeasurement model is then trained based on the spectral measurement dataand an expected response model for focus, exposure, MCD for an isolatedtarget, and MCD for a dense target, respectively. The resulting trainedmeasurement models are subsequently employed to calculate focus,exposure, and MCD for both isolated and dense targets on sample wafers.In this manner, each parameter has its own trained model that calculatesthe parameter value from the measured spectra (or extracted features)associated with both isolated and dense targets.

FIGS. 9A-9B and 10A-10B illustrate measurement results for focus andexposure for FEM and CDU wafers with under layer gratings.

FIG. 9A is a contour plot 161 illustrative of measurements of depth offocus across the surface of a FEM wafer. In this example, focus variesin the x-direction across the wafer and is constant in the y-directionacross the wafer. FIG. 9B is a contour plot 162 illustrative ofmeasurements of exposure dosage across the surface of the FEM wafer. Asillustrated, dosage varies in the y-direction across the wafer and isconstant in the x-direction across the wafer.

The measurement results illustrated in FIGS. 9A and 9B result frommeasurements (i.e., measured spectra) collected at multiple measurementsites on the FEM wafer and processed in accordance with method 140illustrated in FIG. 5. The underlying dosage and focus measurementmodels were developed in accordance with method 110 and trained withmeasurement data collected from different measurement sites on the sameDOE wafer.

FIG. 10A is a contour plot 163 illustrative of measurements of depth offocus across the surface of a sample wafer. FIG. 10B is a contour plot164 illustrative of measurements of exposure dosage across the surfaceof the same wafer described with reference to FIG. 10A. The samplewafers were processed at a particular depth of focus and exposuredosage. The particular focus and dosage values correspond approximatelywith the focus and dosage values near the middle of the FEM wafermeasured in FIGS. 9A-9B. As a result, it is expected that the focus andexposure measurement results illustrated in FIGS. 10A-10B, respectively,show minimal variation in focus and exposure over the wafer surface.

The measurement results illustrated in FIGS. 10A and 10B are derivedfrom measurements (i.e., measured spectra) collected at multiplemeasurement sites on a wafer, different from the FEM wafer, andprocessed in accordance with method 140 illustrated in FIG. 5. Theunderlying dosage and focus measurement models were developed inaccordance with method 110 and trained with measurement data collectedfrom different measurement sites on the DOE wafer described withreference to FIGS. 9A and 9B.

FIG. 11 illustrates a matrix 170 of principal components maps of spectracollected from a FEM wafer. As illustrated, the first few principalcomponents roughly reflect the global focus and exposure patternscreated by the Focus and Exposure Matrix (i.e., variation in onedirection, constant in the orthogonal direction, and vice-versa).Principal component maps higher than seven exhibit a noisy patternassociated with random under layer variations, line edge roughness, orother types of noise. In this example, it is preferred to utilize onlythe first seven principal components to train the focus and exposuremodels. In this manner, principal components that primarily reflectnoise are truncated for purposes of model building, and subsequentmeasurement analysis.

In another further aspect, measurement data derived from measurementsperformed by a combination of multiple, different measurement techniquesis collected for model building, training, and measurement. The use ofmeasurement data associated with multiple, different measurementtechniques increases the sample and process information embedded in themodel and enables more accurate measurements. Measurement data may bederived from measurements performed by any combination of multiple,different measurement techniques. In this manner, different measurementsites may be measured by multiple, different measurement techniques toenhance the measurement information available for characterization ofthe semiconductor structures.

In general, any measurement technique, or combination of two or moremeasurement techniques may be contemplated within the scope of thispatent document. Exemplary measurement techniques include, but are notlimited to spectroscopic ellipsometry, including Mueller matrixellipsometry, spectroscopic reflectometry, spectroscopic scatterometry,scatterometry overlay, beam profile reflectometry, both angle-resolvedand polarization-resolved, beam profile ellipsometry, single or multiplediscrete wavelength ellipsometry, transmission small angle x-rayscatterometer (TSAXS), small angle x-ray scattering (SAXS), grazingincidence small angle x-ray scattering (GISAXS), wide angle x-rayscattering (WAXS), x-ray reflectivity (XRR), x-ray diffraction (XRD),grazing incidence x-ray diffraction (GIXRD), high resolution x-raydiffraction (HRXRD), x-ray photoelectron spectroscopy (XPS), x-rayfluorescence (XRF), grazing incidence x-ray fluorescence (GIXRF), x-raytomography, and x-ray ellipsometry. In general, any metrology techniqueapplicable to the characterization of semiconductor structures,including image based metrology techniques, may be contemplated.

In some examples, the model building, training, and measurement methodsdescribed herein are implemented as an element of a SpectraShape®optical critical-dimension metrology system available from KLA-TencorCorporation, Milpitas, Calif., USA. In this manner, the model is createdand ready for use immediately after the DOE wafer spectra are collectedby the system.

In some other examples, the model building and training methodsdescribed herein are implemented off-line, for example, by a computingsystem implementing AcuShape® software available from KLA-TencorCorporation, Milpitas, Calif., USA. The resulting, trained model may beincorporated as an element of an AcuShape® library that is accessible bya metrology system performing measurements.

Although several examples are described hereinbefore with reference to alithography process model and associated focus and exposure metrologies,the methods and systems described herein may involve other processmodels (e.g., etch or deposition processing), and other metrologies(e.g., etch and deposition metrologies). The methods and systemsdescribed herein may also involve other reference metrology technologies(e.g. SEM, TEM, AFM, X-ray). Moreover, the methods and systems describedherein are discussed with reference to optical metrology systems (e.g.,spectroscopic ellipsometers, reflectometers, BPR systems, etc.), but canbe also applied to other model-based metrologies (e.g., overlay,CD-SAXS, XRR, etc.).

In yet another aspect, the measurement model results described hereincan be used to provide active feedback to a process tool (e.g.,lithography tool, etch tool, deposition tool, etc.). For example, valuesof the depth of focus parameters determined using the methods describedherein can be communicated to a lithography tool to adjust thelithography system to achieve a desired output. In a similar way etchparameters (e.g., etch time, diffusivity, etc.) or deposition parameters(e.g., time, concentration, etc.) may be included in a measurement modelto provide active feedback to etch tools or deposition tools,respectively.

In general, the systems and methods described herein can be implementedas part of the process of preparing a measurement model for off-line oron-tool measurement. In addition, both measurement models and anyreparameterized measurement model may describe one or more targetstructures and measurement sites.

As described herein, the term “critical dimension” includes any criticaldimension of a structure (e.g., bottom critical dimension, middlecritical dimension, top critical dimension, sidewall angle, gratingheight, etc.), a critical dimension between any two or more structures(e.g., distance between two structures), and a displacement between twoor more structures (e.g., overlay displacement between overlayinggrating structures, etc.). Structures may include three dimensionalstructures, patterned structures, overlay structures, etc.

As described herein, the term “critical dimension application” or“critical dimension measurement application” includes any criticaldimension measurement.

As described herein, the term “metrology system” includes any systememployed at least in part to characterize a specimen in any aspect,including measurement applications such as critical dimension metrology,overlay metrology, focus/dosage metrology, and composition metrology.However, such terms of art do not limit the scope of the term “metrologysystem” as described herein. In addition, the metrology system 300 maybe configured for measurement of patterned wafers and/or unpatternedwafers. The metrology system may be configured as a LED inspection tool,edge inspection tool, backside inspection tool, macro-inspection tool,or multi-mode inspection tool (involving data from one or more platformssimultaneously), and any other metrology or inspection tool thatbenefits from the calibration of system parameters based on criticaldimension data.

Various embodiments are described herein for a semiconductor processingsystem (e.g., an inspection system or a lithography system) that may beused for processing a specimen. The term “specimen” is used herein torefer to a wafer, a reticle, or any other sample that may be processed(e.g., printed or inspected for defects) by means known in the art.

As used herein, the term “wafer” generally refers to substrates formedof a semiconductor or non-semiconductor material. Examples include, butare not limited to, monocrystalline silicon, gallium arsenide, andindium phosphide. Such substrates may be commonly found and/or processedin semiconductor fabrication facilities. In some cases, a wafer mayinclude only the substrate (i.e., bare wafer). Alternatively, a wafermay include one or more layers of different materials formed upon asubstrate. One or more layers formed on a wafer may be “patterned” or“unpatterned.” For example, a wafer may include a plurality of dieshaving repeatable pattern features.

A “reticle” may be a reticle at any stage of a reticle fabricationprocess, or a completed reticle that may or may not be released for usein a semiconductor fabrication facility. A reticle, or a “mask,” isgenerally defined as a substantially transparent substrate havingsubstantially opaque regions formed thereon and configured in a pattern.The substrate may include, for example, a glass material such asamorphous SiO₂. A reticle may be disposed above a resist-covered waferduring an exposure step of a lithography process such that the patternon the reticle may be transferred to the resist.

One or more layers formed on a wafer may be patterned or unpatterned.For example, a wafer may include a plurality of dies, each havingrepeatable pattern features. Formation and processing of such layers ofmaterial may ultimately result in completed devices. Many differenttypes of devices may be formed on a wafer, and the term wafer as usedherein is intended to encompass a wafer on which any type of deviceknown in the art is being fabricated.

In one or more exemplary embodiments, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored on ortransmitted over as one or more instructions or code on acomputer-readable medium. Computer-readable media includes both computerstorage media and communication media including any medium thatfacilitates transfer of a computer program from one place to another. Astorage media may be any available media that can be accessed by ageneral purpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code means in the form of instructions or datastructures and that can be accessed by a general-purpose orspecial-purpose computer, or a general-purpose or special-purposeprocessor. Also, any connection is properly termed a computer-readablemedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition of medium.Disk and disc, as used herein, includes compact disc (CD), laser disc,optical disc, digital versatile disc (DVD), floppy disk and blu-ray discwhere disks usually reproduce data magnetically, while discs reproducedata optically with lasers. Combinations of the above should also beincluded within the scope of computer-readable media.

Although certain specific embodiments are described above forinstructional purposes, the teachings of this patent document havegeneral applicability and are not limited to the specific embodimentsdescribed above. Accordingly, various modifications, adaptations, andcombinations of various features of the described embodiments can bepracticed without departing from the scope of the invention as set forthin the claims.

What is claimed is:
 1. A method comprising: illuminating a firstplurality of measurement sites of one or more semiconductor wafers withillumination light generated by an illumination source, the firstplurality of measurement sites having known variations of at least oneprocess parameter, structural parameter, or both; detecting lightcollected from each of the first plurality of measurement sites inresponse to the illumination light with a spectrometer; generating ameasured spectral response of each of the plurality of measurement sitesfrom the light detected by the spectrometer in accordance with at leastone metrology technique, the measured spectral response comprising afirst amount of measurement data; determining an expected response modelof each of the at least one known process parameters, structureparameters, or both; determining an input-output measurement model basedat least in part on the first amount of measurement data; training theinput-output measurement model based on parameter values determined fromthe expected response model; receiving a second amount of measurementdata associated with measurements of a second plurality of sites usingthe at least one metrology technique; determining at least one processparameter value, at least one structural parameter value, or both,associated with each of the second plurality of sites based on a fittingof the second amount of measurement data to the trained input-outputmeasurement model; and communicating an indication of the at least oneprocess parameter value, the at least one structural parameter value, orboth, to a semiconductor fabrication tool that causes the semiconductorfabrication tool to adjust one or more parameters of a fabricationprocess of the semiconductor fabrication tool to achieve a desiredoutput from the fabrication tool.
 2. The method of claim 1, furthercomprising: storing any of the at least one process parameter value, theat least one structural parameter value, or both, in a memory.
 3. Themethod of claim 1, wherein the first amount of measurement data isassociated with measurements of a first plurality of sites with knownvariations of focus and exposure dosage over the surface of thesemiconductor wafer.
 4. The method of claim 1, further comprising:extracting one or more features of the first amount of measurement databy reducing a dimension of the first amount of measurement data, andwherein the determining the input-output measurement model is based atleast in part on the one or more features.
 5. The method of claim 4,wherein the reducing the dimension of the first amount of measurementdata involves any of a principal components analysis, a non-linearprincipal components analysis, a selection of individual signals fromthe first amount of measurement data, and a filtering of the firstamount of measurement data.
 6. The method of claim 1, wherein theexpected response model is a wafer map model, and wherein thedetermining the wafer map model involves fitting a two dimensional mapfunction to the known process parameters, structure parameters, or both,associated with the first plurality of sites.
 7. The method of claim 1,further comprising: determining the at least one known structureparameter based at least in part on a simulation of a process performedon the semiconductor wafer.
 8. The method of claim 1, furthercomprising: determining the at least one known structure parameter basedat least in part on an amount of reference measurement data associatedwith the at least one known structure parameter.
 9. The method of claim1, wherein the first amount of measurement data includes measurementsignals associated with more than one target feature at any of the firstplurality of sites.
 10. The method of claim 1, wherein the first amountof measurement data includes measurement signals associated with morethan one metrology technique.
 11. A system comprising: an illuminatorthat provides illumination light to a first plurality of measurementsites disposed on a surface of a semiconductor wafer, the firstplurality of measurement sites having known variations of at least oneprocess parameter, structural parameter, or both; a spectrometer thatdetects light collected from each of the first plurality of measurementsites of the semiconductor wafer in response to the illumination lightprovided to the semiconductor wafer and generates a measured spectralresponse of each of the plurality of measurement sites of thesemiconductor wafer from the detected light in accordance with at leastone metrology technique, the measured spectral response comprising afirst amount of measurement data; and a computing system configured to:determine an expected response model of each of the at least one knownprocess parameters, structure parameters, or both; determine aninput-output measurement model based at least in part on the firstamount of measurement data; train the input-output measurement modelbased on parameter values determined from the expected response model;receive a second amount of measurement data associated with measurementsof a second plurality of sites; determine at least one process parametervalue, at least one structural parameter value, or both, associated witheach of the second plurality of sites based on a fitting of the secondamount of measurement data to the trained input-output measurementmodel; and communicate an indication of the at least one processparameter value, the at least one structural parameter value, or both,to a semiconductor fabrication tool that causes the semiconductorfabrication tool to adjust one or more parameters of a fabricationprocess of the semiconductor fabrication tool to achieve a desiredoutput from the fabrication tool.
 12. The system of claim 11, whereinthe computing system is further configured to: store any of the at leastone process parameter value, the at least one structural parametervalue, or both, in a memory.
 13. The system of claim 11, wherein thecomputing system is further configured to: extract one or more featuresof the first amount of measurement data by reducing a dimension of thefirst amount of measurement data, and wherein the determining theinput-output measurement model is based at least in part on the one ormore features.
 14. The system of claim 13, wherein the reducing thedimension of the first amount of measurement data involves any of aprincipal components analysis, a non-linear principal componentsanalysis, a selection of individual signals from the first amount ofmeasurement data, and a filtering of the first amount of measurementdata.
 15. The system of claim 11, wherein the expected response model isa wafer map model, and wherein the determining the wafer map modelinvolves fitting a two dimensional map function to the known processparameters, structure parameters, or both, associated with the firstplurality of sites.
 16. The system of claim 11, wherein the first amountof measurement data includes measurement signals associated with morethan one target feature at any of the first plurality of sites.
 17. Thesystem of claim 11, wherein the first amount of measurement dataincludes measurement signals associated with more than one metrologytechnique.
 18. A method comprising: illuminating a first plurality ofmeasurement sites on one or more semiconductor wafers with illuminationlight generated by an illumination source, the first plurality ofmeasurement sites having known variations of at least one processparameter, structural parameter, or both; detecting light collected fromeach of the first plurality of measurement sites in response to theillumination light with a spectrometer; generating a measured spectralresponse of each of the plurality of measurement sites from the lightdetected by the spectrometer, the measured spectral response comprisinga first amount of measurement data; determining an expected responsemodel of each of the at least one known process parameters, structureparameters, or both; determining an input-output measurement model basedat least in part on the first amount of measurement data; training theinput-output measurement model based on parameter values determined fromthe expected response model; illuminating a second plurality ofmeasurement sites on one or more semiconductor wafers with illuminationlight generated by the illumination source, the second plurality ofmeasurement sites having unknown variations of at least one processparameter, structural parameter, or both; detecting light collected fromeach of the second plurality of measurement sites in response to theillumination light with the spectrometer; generating a measured spectralresponse of each of the second plurality of measurement sites of thesemiconductor wafer from the light detected by the spectrometer, themeasured spectral response comprising a second amount of measurementdata; determining at least one process parameter value, at least onestructural parameter value, or both, based on a fitting of the secondamount of measurement data to the trained input-output measurementmodel; and communicating an indication of the at least one processparameter value, the at least one structural parameter value, or both,to a semiconductor fabrication tool that causes the semiconductorfabrication tool to adjust one or more parameters of a fabricationprocess of the semiconductor fabrication tool to achieve a desiredoutput from the fabrication tool.
 19. The method of claim 18, furthercomprising: extracting one or more features of the first amount ofmeasurement data by reducing a dimension of the first amount ofmeasurement data, and wherein the determining the input-outputmeasurement model is based at least in part on the one or more features;and extracting one or more features of the second amount of measurementdata by reducing a dimension of the second amount of measurement data,and wherein the determining the at least one process parameter value, atleast one structural parameter value, or both, is based at least in parton the one or more features.